An mcdm-based approach to compare the performance of heuristic techniques for permutation flow-shop scheduling problems

In the industrial and manufacturing sectors, scheduling is an essential component in the process of determining crucial production cost aspects of corporate strategy. Solving flow-shop problems minimizes the makespan it takes for all jobs to be completed, reducing production costs and boosting output. Therefore, many heuristics techniques have been developed to assist in reaching a good and quick solution. However, newly developed techniques necessitate testing their performance against the classical ones. Therefore, this paper aims to conduct a comparative analytical, computational study of heuristic techniques for solving Permutation Flow-Shop Sequencing Problems and evaluating their performance. Eight techniques were compared by generating a set of problems of varying sizes and then solving them via a developed computer simulation program. Furthermore, a multi-criteria decision-making approach is followed for their performance evaluation. Results of the study revealed that based on six performance evaluation criteria, Dannenbring's technique is the first best, followed by the Slope Index technique as the second best, then the technique by Campbell, Dudek, and Smith, Hundal, the Time Deviation technique, Palmer, Gupta, and the technique by Jayasankari, Jayakumar, and Vijayaragavan, respectively. This paper puts forward a ranking of the developed techniques for flow-shop problems and a framework for the performance evaluation of new permutation flow-shop scheduling problem methods.